Articles | Volume 13, issue 3
Research article
06 Sep 2022
Research article |  | 06 Sep 2022

Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Nicola Maher, Thibault P. Tabarin, and Sebastian Milinski

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Cited articles

An, S.-I. and Wang, B.: Interdecadal Change of the Structure of the ENSO Mode and Its Impact on the ENSO Frequency, J. Climate, 13, 2044–2055,<2044:ICOTSO>2.0.CO;2, 2000. a, b
Ashok, K., Behera, S. K., Rao, S. A., Weng, H., and Yamagata, T.: El Niño Modoki and its possible teleconnection, J. Geophys. Res.-Oceans, 112, C11007,, 2007. a
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Viewing Forced Climate Patterns Through an AI Lens, Geophys. Res. Lett., 46, 13389–13398,, 2019. a
Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Indicator Patterns of Forced Change Learned by an Artificial Neural Network, J. Adv. Model. Earth Sy., 12, e2020MS002195,, 2020. a
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018,, 2014. a
Short summary
El Niño events occur as two broad types: eastern Pacific (EP) and central Pacific (CP). EP and CP events differ in strength, evolution, and in their impacts. In this study we create a new machine learning classifier to identify the two types of El Niño events using observed sea surface temperature data. We apply our new classifier to climate models and show that CP events are unlikely to change in frequency or strength under a warming climate, with model disagreement for EP events.
Final-revised paper